Confident learning: Estimating uncertainty in dataset labels
… we discuss four key differences between confident learning and INCV. First, INCV errors are
found using an iterative version of the Cconfusion confident learning baseline: any example …
found using an iterative version of the Cconfusion confident learning baseline: any example …
Characterizing label errors: confident learning for noisy-labeled image segmentation
… Creatively, We introduce confident learning (CL) method to identify the corrupted labels
and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is …
and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is …
Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation
… -assisted confident learning framework to robustly exploit the noisy labeled data for the
challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning …
challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning …
Some philosophical concerns about the confidence in 'confident learning'
M Friend - Induction, Algorithmic Learning Theory, and …, 2007 - Springer
… section, a discussion about what confident learning is which will mainly consist in definitions…
model theory which underpins the problem with “confident learning”, and finally, a section on …
model theory which underpins the problem with “confident learning”, and finally, a section on …
Efficientclip: Efficient cross-modal pre-training by ensemble confident learning and language modeling
… In this work, we propose an EfficientCLIP method via Ensemble Confident Learning to obtain
a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting …
a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting …
Anti-interference from noisy labels: Mean-teacher-assisted confident learning for medical image segmentation
… To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL)
framework constructed by a teacher-student architecture and a label self-denoising process to …
framework constructed by a teacher-student architecture and a label self-denoising process to …
Confident Learning for Machines and Humans
CG Northcutt - 2021 - dspace.mit.edu
… To this end, we introduce confident learning whereby a machine (like humans) must learn
… We achieve this by developing a principled theory and framework for confident learning with …
… We achieve this by developing a principled theory and framework for confident learning with …
Mitigating label bias in machine learning: Fairness through confident learning
… This paper demonstrates that bias can be eliminated by implementing confident learning
and filtering the fairest instances, even with access to biased labels. However, challenges arise …
and filtering the fairest instances, even with access to biased labels. However, challenges arise …
Adaptive and self-confident on-line learning algorithms
We study on-line learning in the linear regression framework. Most of the performance bounds
for on-line algorithms in this framework assume a constant learning rate. To achieve these …
for on-line algorithms in this framework assume a constant learning rate. To achieve these …
Confident learning-based domain adaptation for hyperspectral image classification
Z Fang, Y Yang, Z Li, W Li, Y Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
… Inspired by confident learning, we rank the pseudo-labels of … framework in combination with
confident learning, which can … target instances by using confident learning so that the target …
confident learning, which can … target instances by using confident learning so that the target …
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